The following relates to the multi-task machine learning arts, classification arts, and so forth, and to arts employing same such as data mining, document indexing and archiving, and so forth.
In applications such as data mining and document archiving, a large quantity of items (e.g., documents) is provided, and various tasks are to be performed by machine learning of suitable classifiers or decision rules. For example, during the discovery phase of a legal dispute it may be necessary for a party to identify documents relevant to various aspects relating to the matter or matters in dispute. Each identification task can be performed by learning a suitable classifier or decision rule via machine learning. (In some applications this may serve as an initial automated “screening” procedure, after which the identified documents are manually reviewed for relevance by appropriate personnel before being turned over to the other side to fulfill discovery requirements).
As another example, in document archiving it is desired to identify the index classifications to which each document belongs. Again, suitable index classification rules may be learned via machine learning and then applied to provide automated indexing of incoming documents for use in archiving.
In these and diverse other applications, the machine learning amounts to multi-task machine learning, where each task corresponds to generating a particular decision rule or classifier for solving a particular task. Thus, for example, each discovery aspect is a task, or each index classification is a task. One approach for performing multi-task machine learning is to learn the rule or classifier for each task independently.
However, if there is common knowledge that is relevant to the different tasks, then it may be desirable to perform “multi-task” machine learning in which the decision rules or classifiers for the various tasks are learned together in a way that leverages the common knowledge. Multi-task machine learning typically assumes that there is some “relatedness” between the different learning tasks, that is, some correlation between the different (but related) tasks as applied in the instance space of interest.
The following sets forth improved methods and apparatuses.